Ugur Alganci, Dursun Seker, and Elif Sertel

“The rapid population growth and related urbanization in developing countries mostly affect the metropolitan cities. The urban sprawl that comes with rapid population growth changes land use / cover (LULC) dynamics that mostly results with decrease in natural resources such as forests or pastures and farmlands. In the last decade, a common population growth is observed all over Turkey with increase at 66 provinces out of 81. Antalya is one of the important cities of Turkey that exposed to unplanned urban sprawl. It was at the 29th place according to 1927 population census while it rose to 7th place according to 2000 census results. According to “9th Progress Report” issued by Ministry of Development, Antalya is the city that exposed to fastest population growth with a rate of %0, 48 during 1990-2000.
Antalya is the most popular touristic city of the Turkey, with its 650 km of coastline, appropriate climatic conditions, historical and natural beauties. Its population has increased due to employment facilities and migration related to tourism. It is also a n important agricultural center with its fertile soil and rainy climate. Development in industry and building trade also results with continuous development and rapid urbanization. Main effects of population growth and related urbanization are; change of watersheds, forests and agricultural lands into settlement in an irregular and uncontrolled way and urban sprawl.

“In terms of effective analysis of urbanization; determination of spatial distribution and trend, effects on other land cover types and relationship between topographic parameters become very important. At this point, spatial and topographic information derived from satellite imagery can be used as main data resources. Images obtained from optical satellites that are capable of observing large areas fastly and repeatedly, have been used for decomposition of land cover types and time dependent changes of them depending on spatial, spectral and temporal resolution capabilities. Spatio-temporal analysis of satellite images provides valuable information about changes in LULC dynamics.

“In this research, the effects of urban sprawl over LULC dynamics of Antalya were investigated in terms of spatial and areal changes using multitemporal satellite imagery. Landsat 5 TM images belonging to 1984, 2001 and 2010 were subjected to registration and supervised classification process. LULC information derived from these analyses was used for areal changes between years. Topographic parameters such as aspect and slope were produced from ASTER GDEM data at the same time. After applying a raster –vector conversation to whole dataset, relationship between LULC changes and spatial-topographic characteristics of the area was determined.”

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Ammon Boswell, Steven Petersen, Ryan Jensen, Danny Summers, and Jason Vernon

“Long-term rangeland monitoring is essential for land managers to make informed and effective decisions. However, most management agencies are responsible for extensive areas, making effective monitoring both time consuming and expensive. Therefore, methods are needed for rangeland monitoring that are rapid, cost effective, accurate and robust. Remote sensing and GIS are tools that have been suggested to provide similar results of plant and bare ground cover as ground-based reference data with an acceptable amount of error. The purpose of this study is to compare plant community data obtained from four different remote sensing platforms with ground reference data collected from field plots in northern Utah. Remote Sensing platforms include Landsat (30m), NAIP (1m), High Resolution remote sensed imagery (0.26m), and very high resolution remote sensed imagery (0.06cm), Total percent cover were determined for trees, shrubs, herbaceous plants, and percent bare ground from each data source. Preliminary results indicate that total tree and shrub cover are discernable from high resolution imagery. Herbaceous plants and percent bare ground were more difficult to discern in comparison to ground-based reference data. This may be due to differences in vegetation sampling techniques. Using remote sensing, managers can monitor broader landscapes at more frequent intervals making it possible to effectively monitor plant community change, the invasion of weedy species, and the effects of disturbance on ecological structure.”

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Sapana Lohani, Chandra Holifield Collins, Philip Heilman, and Ronald L. Tiller

“State and transition models (STMs) have been gaining momentum in rangeland management. STMs are theoretical depictions of the variation due to climate, management, or both, of stable plant communities within ecological sites. For widespread application, maps of the vegetation states presented within these models are needed to allow managers to not only make better informed decisions about what management practices to employ to improve or maintain their rangelands, but to assess the effectiveness of management practices. This study used high-resolution satellite imagery and ground-based data to map vegetation states within identified ecological sites on the Empire Ranch, located within the Las Cienegas National Conservation Area (LCNCA) in southeastern Arizona. The resulting state map was used to assess the effectiveness of brush management practices to drive vegetation communities on several ecological sites from one state to another. The combination of remotely-sensed images, field monitoring, and state and transition models shows great potential as a means of mapping states and evaluating the benefits of established management practices to drive transitions from one vegetation state to another across large areas.”

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International Journal of Remote Sensing

Enton Bedini

“The Malmbjerg molybdenum deposit in central East Greenland is a world-class porphyry molybdenum deposit. The porphyry molybdenum deposit occurs within a granite stock intruded into sedimentary rocks. The Malmbjerg molybdenum deposit is associated with a pronounced zone of hydrothermal alteration. The reflectance spectra of rock samples from the Malmbjerg alteration assemblage show absorption features of a number of minerals including topaz, jarosite, goethite, muscovite, phengite, epidote, chlorite and smectite. This study investigated the Malmbjerg alteration assemblage using airborne imaging spectrometer data recorded by the HyMap imaging system. The HyMap data were analysed using an unsupervised classification based on Kohonen self-organizing maps and partial spectral unmixing based on the matched filter algorithm. The mapping results show the spatial distribution of jarosite, goethite, phengite, epidote/chlorite, smectite, topaz and non-altered sediments. The remote-sensing mapping results bring new information for the alteration assemblage at Malmbjerg, especially for the occurrence and the spatial distribution of a phengite zone in the altered sediments overlying the molybdenum deposit and for the propylitic alteration zone. The study is an example of detailed characterization by imaging spectrometry of alteration assemblages associated with porphyry molybdenum deposits. This research also shows the potential of imaging spectrometry as a tool for geological mapping and exploration in the Arctic regions of East Greenland.”

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International Journal of Remote Sensing

Zhi Huang and Xiuping Jia

“Remote-sensing technology provides a powerful means for land use/land cover (LU/LC) monitoring at global and regional scales. However, it is more efficient and effective to combine remote-sensing measurements with a geographic information system (GIS) database and expert knowledge for change updating than to use remote-sensing technology alone. In this article, these different sources of information are integrated in the proposed framework, which is able to provide rapid updating of LU/LC information. An object-based data analysis is adopted for thematic mapping, taking both spectral and spatial properties into consideration. An expert knowledge coding is introduced and combined quantitatively with other evidence provided by remotely sensed data and the GIS database. A case study using Landsat Thematic Mapper (TM) datasets demonstrated an overall successful LU/LC map updating and a satisfactory change detection using the proposed change-updating framework.”

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Applied Geography, Volume 34, May 2012

Richard Charrier and Yingkui Li

“Highlights

• We examined resolution and source effects of DEMs on floodplain delineation.
• High discrepancies exist between features delineated by LiDAR and USGS DEMs.
• High resolution DEMs are more sensitive to minor features in watershed delineation.
• LiDAR DEMs produce more resolution-dependent delineations.
• USGS DEMs produce similar delineations regardless of DEM resolutions (5–30 m).

“Digital elevation models (DEMs) have been widely used in automated floodplain modeling to determine floodplain boundaries. However, the effects of DEM resolution and data source on floodplain delineation are not well quantified. This paper presents a case study to assess these effects from the Camp Creek Watershed, Missouri, using two sets of DEMs. One is the Light Detection and Ranging (LiDAR) DEMs re-sampled from 1-m to 3, 5, 10, 15, and 30-m resolutions. The other is 5, 10, and 30-m DEMs obtained from the U.S. Geological Survey (USGS). Floodplain boundaries are delineated using a combination of hydrological, hydraulic and floodplain delineation models under the Federal Emergency Management Agency’s (FEMA) guideline. Model outputs including stream network, watershed and floodplain boundaries are compared to 1-m LiDAR DEM outputs (as the reference) to assess the uncertainty. Results indicate that re-sampled 3 or 5-m LiDAR DEMs produce similar streams and floodplain boundaries within 10% difference of the reference. In contrast, coarser LiDAR DEMs (such as the 10-m resolution) are more appropriate for watershed boundary delineation because higher DEM resolutions are likely more sensitive to minor topographic changes and may introduce erroneous boundaries. For different data sources, uncertainties introduced by USGS DEMs are much higher than LiDAR DEMs with a distinct relationship between uncertainties and DEM resolutions. Uncertainties of LiDAR DEMs consistently increase with decreasing resolutions, whereas similar levels of uncertainty are observed for different USGS DEM resolutions. This difference is probably due to the inherited difference in their original data source resolutions to make these two types of DEMs.”

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Applied Geography, Volume 34, May 2012

Jesus Aguirre-Gutiérrez, Arie C. Seijmonsbergen, and Joost F. Duivenvoorden

“Highlights

• The land cover classification accuracy is optimized with a combined approach.
• The optimized classification is the input in a change detection analysis.
• This method produces higher classification and change detection accuracies.
• This approach can potentially be applied to other mountainous regions.

“Inventories of past and present land cover changes form the basis of future conservation and landscape management strategies. Modern classification techniques can be applied to more efficiently extract information from traditional remote-sensing sources. Landsat ETM⁺ images of a mountainous area in Mexico form the input for a combined object-based and pixel-based land cover classification. The land cover categories with the highest individual classification accuracies determined based on these two methods are extracted and merged into combined land cover classifications. In total, seven common land cover categories were recognized and merged into single combined best-classification layers. A comparison of the overall classification accuracies for 1999 and 2006 of the pixel-based (0.74 and 0.81), object-based (0.77 and 0.71) and combined (0.88 and 0.87) classifications shows that the combination method produces the best results. These combined classifications then form the input for a change detection analysis between the two dates by applying post-classification, object-based change analysis using image differencing. It is concluded that the combined classification method together with the object-based change detection analysis leads to an improved classification accuracy and land cover change detection. This approach has the potential to be applied to land cover change analyses in similar mountainous areas using medium-resolution imagery.”

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GIScience & Remote Sensing

Sean Griffin, John Rogan, and Daniel Miller Runfola

“This paper compares a variety of classification tree-based approaches to map 10 vegetation cover classes and a single built-up class in the Kissimmee Prairie Ecosystem, an endangered grass-shrubland landscape in south-central Florida (USA). This comparison is provided to identify an effective and replicable mapping methodology and facilitate the ongoing regional-scale management and monitoring of grass-shrubland ecosystems. Results showed that the best-performing models included environmental variables, due to the ability of these variables to help distinguish spectrally similar classes. The highest overall proportional accuracy of 81% was the result of incorporating linear spectral mixture analysis and geo-environmental variables into the classification tree.”

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Researchers say the method could be used to control other diseases such as meningitis that spread quickly through dense populations.

The research team, including a scientist from the University of Florida, analyzed nighttime satellite images of three cities in Niger taken between 2000 and 2004, and found that seasonal population surges correlated strongly with local measles epidemic outbreaks recorded for the same period. The epidemics kill thousands annually in West Africa, although the disease has been largely eradicated in the U.S. through long-standing immunization programs.

The research, published in the Dec. 9 issue of the journal Science, shows that satellite images of city lights can reliably predict a likely outbreak by indicating where the highest concentrations of people are. In Niger, measles outbreaks occur when people crowd into the cities during the dry season each year to find work. Until now, there has been no way to assess how many people were moving into the cities and where the highest concentrations of people were.

“In the U.S., light levels would saturate satellite imagery to the point that it couldn’t tell us much about the details of population distribution within a city,” said study co-author Andrew Tatem, a UF assistant professor of geography who specializes in spatial data modeling and analysis. “But in Niger, when people gather in numbers and turn on electric lighting or light fires at night, you can see it from outer space.”

The researchers used data from the National Oceanic and Atmospheric Administration that is available to the public online, but finding enough moonless, cloud-free images of Niger’s night sky was a challenge.

“The satellite data gave us an entirely different kind of information that we didn’t have before,” said Nita Bharti, the study’s lead author and a postdoctoral researcher at Princeton University. Each of the 150 images chosen for the study was a snapshot in time. But viewed chronologically, the images showed how the concentration of people shifted from rural areas into the city.

The association between high population density and the spread of measles is well documented in pre-vaccination industrialized nations, but only suspected in Niger, where a lack of infrastructure and poorly understood migratory populations make traditional immunization programs a challenge, she said. Bharti and her team worked closely with Niger’s minister of health during the study, and the measles vaccination unit leader from Epicentre, the research branch of Doctors without Borders, France.

“This isn’t a trivial piece of work,” said Peter Hudson, co-founder of the Center for Infectious Disease Dynamics at Pennsylvania State University who was not on the research team. “This approach takes us closer to being able to predict and prevent outbreaks.”

Bharti and Tatem said they plan to continue their collaboration and work to make the approach more accessible for health care organizations and disease outbreak response teams who need the information.

“The project was more of a practical application drill than an academic study,” Bharti said. “We wanted to see if the satellite data could be used this way.”

[Source: University of Florida press release]

Applied Geography, Volume 34, May 2012

Guillermo C. Martínez-Verduzco, J. Mauricio Galeana-Pizaña, and Gustavo M. Cruz-Bello

“Highlights

• We coupled supervised classification with Community Mapping to separate Shade coffee and forest.
• We elicited local knowledge from study area inhabitants through participatory workshops.
• This coupled method produced similar accuracy levels to supervised classification.
• This combined method demonstrated being less time and resources consuming.

“Discriminating between Shade coffee plantations and Natural vegetation using Remote Sensing is particularly difficult in zones where both coverages have almost the same mix of species, as is the case in several areas of the Chiapas highlands. This investigation couples supervised classification with Community Mapping to separate these vegetation classes. Local knowledge of the study area was elicited from local inhabitants through workshops. The participants were asked to delimit both coverages inside the areas they knew the best (confidence map) with the help of printed orthophotos to build a land use map. The accuracy of this coupled method was similar to supervised classification alone and with less time and resources invested. This method can be applied in the rural zones of developing countries, as it is easy to understand and is cheaper than similar alternatives.”

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